1 code implementation • 23 Apr 2024 • Xiongxiao Xu, Yueqing Liang, Baixiang Huang, Zhiling Lan, Kai Shu
In this paper, we propose to leverage a hybrid framework Mambaformer that internally combines Mamba for long-range dependency, and Transformer for short range dependency, for long-short range forecasting.
no code implementations • 24 Mar 2024 • Boyang Li, Zhiling Lan, Michael E. Papka
In this work, we present a framework called IRL (Interpretable Reinforcement Learning) to address the issue of interpretability of DRL scheduling.
no code implementations • 16 May 2021 • Yuping Fan, Zhiling Lan
For decades, system administrators have been striving to design and tune cluster scheduling policies to improve the performance of high performance computing (HPC) systems.
1 code implementation • 11 Feb 2021 • Yuping Fan, Zhiling Lan, Taylor Childers, Paul Rich, William Allcock, Michael E. Papka
Existing cluster scheduling heuristics are developed by human experts based on their experience with specific HPC systems and workloads.
no code implementations • 12 Nov 2020 • Xingfu Wu, Valerie Taylor, Zhiling Lan
In this paper, we use modeling and prediction tool MuMMI (Multiple Metrics Modeling Infrastructure) and ten machine learning methods to model and predict performance and power and compare their prediction error rates.